Life-threatening arrhythmias recognition by pulse-to-pulse intervals analysis

Abstract

Tachycardia, bradycardia, ventricular flutter and ventricular tachycardia are the four life-threatening arrhythmias, which are seriously harmful to the cardiovascular system. Therefore, a method for identifying these arrhythmias by pulse-to-pulse intervals analysis is proposed in this study. First, the noise and interference are wiped out from the raw pulse signal, and the clear pulse signal is spilt into pulse waves by pulse troughs whose first-order difference is the pulse-to-pulse intervals. Then, 15 features are extracted from the pulse-to-pulse intervals, and the two-samples Kolmogorov-Smirnov test is utilised to select the markedly changed features. Finally, we design the classifiers for arrhythmias recognition by the Probabilistic Neural Network (PNN), feedback neural network (BPNN) and Random Forest (RF). The pulse signal from the international physiological database (PhysioNET) is utilised as the experimental data. The experimental results show that RF classifier has the best average classification performance with the Kappa Coefficient (KC) of 98.86 ± 0.13% which is higher than that of BPNN with KC of 70.70 ± 1.61% and PNN with KC of 62.07 ± 0.75%. Compared with the existing methods, the proposed method has a higher performance in recognition of the four arrhythmias and has great potential to monitor the life-threatening arrhythmia in m-health.